Working with Discourse Representation Theory Patrick Blackburn & Johan Bos Lecture 5 Applying DRT

1. Working with Discourse Representation TheoryPatrick Blackburn & Johan BosLecture 5Applying DRT

2. Today • Given what we know about DRT, both from a theoretical and practical perspective, can we use it for practical applications?

3. Outline • Spoken dialogue system with DRT • Using DRT and inference to control a mobile robot • Wide coverage parsing with DRT • Recognising Textual Entailment

4. Human-Computer Dialogue • Focus on small domains • Grammatical coverage ensured • Background knowledge encoding • Spoken Dialogue system • Godot the robot • Speech recognition and synthesis • People could give Godot directions, ask it questions, tell it new information • Godot was a REAL robot

5. Godot the Robot Godot with Tetsushi Oka

6. Typical conversation with Godot • Simple dialogues in beginning • Human: Robot? • Robot: Yes? • Human: Where are you? • Robot: I am in the hallway. • Human: OK. Go to the rest room!

7. Adding DRT to the robot • The language model that the robot used for speech recognition returned DRSs • We used the model builder MACE and the theorem prover SPASS for inference • The model produced by MACE was used to find out what the robot should do • This was possible as MACE produces minimal models • Of course we also checked for consistency and informativity

8. Advanced conversation with Godot • Dealing with inconsistency and informativity • Human: Robot, where are you? • Robot: I am in the hallway. • Human: You are in my office. • Robot: No, that is not true. • Human: You are in the hallway. • Robot: Yes I know!

9. Videos of Godot Video 1: Godot in the basement of Bucceuch Place Video 2: Screenshot of dialogue manager with DRSs and camera view of Godot

10. Minimal Models • Model builders normally generate models by iteration over the domain size • As a side-effect, the output is a model with a minimal domain size • From a linguistic point of view, this is interesting, as there is no redundant information

11. Using models • Examples:Turn on a light.Turn on every light.Turn on everything except the radio. Turn off the red light or the blue light.Turn on another light.

12. Adding presupposition • Godot was connected to an automated home environment • One day, I asked Godot to switch on all the lights • However, Godot refused to do this, responding that it was unable to do so. • Why was that? • At first I thought that the theorem prover made a mistake. • But it turned out that one of the lights was already on.

13. Intermediate Accommodation • Because I had coded to switch on X having a precondition that X is not on, the theorem prover found a proof. • Coding this as a presupposition, would not give an inconsistency, but a beautiful case of intermediate accommodation. • In other words: • Switch on all the lights![ All lights are off; switch them on.][=Switch on all the lights that are currently off]

14. Sketch of resolution

15. Global Accommodation

16. Intermediate Accommodation

17. Local Accommodation

18. Outline • Spoken dialogue system with DRT • Using DRT and inference to control a mobile robot • Wide coverage parsing with DRT • Recognising Textual Entailment

19. Wide-coverage DRT • Nowadays we have robust wide-coverage parsers that use stochastic methods for producing a parse tree • Trained on Penn Tree Bank • Examples are parsers like those from Collins and Charniak

20. Wide-coverage parsers • Say we wished to produce DRSs on the output of these parsers • We would need quite detailed syntax derivations • Closer inspection reveals that many of the parsers use many [several thousands] phrase structure rules • Often, long distance dependencies are not recovered

21. Combinatory Categorial Grammar • CCG is a lexicalised theory of grammar (Steedman 2001) • Deals with complex cases of coordination and long-distance dependencies • Lexicalised, hence easy to implement • English wide-coverage grammar • Fast robust parser available

22. Categorial Grammar • Lexicalised theory of syntax • Many different lexical categories • Few grammar rules • Finite set of categories defined over a base of core categories • Core categories: s np n pp • Combined categories: np/n s\np (s\np)/np

23. CCG: type-driven lexicalised grammar

24. CCG: combinatorial rules • Forward Application (FA) • Backward Application (BA) • Generalised Forward Composition (FC) • Backward Crossed Composition (BC) • Type Raising (TR) • Coordination

25. CCG derivation NP/N:aN:spokesmanS\NP:lied

26. CCG derivation NP/N:a N:spokesman S\NP:lied

27. CCG derivation NP/N:a N:spokesman S\NP:lied ------------------------------- (FA)

28. CCG derivation NP/N:aN:spokesman S\NP:lied ------------------------------- (FA) NP: a spokesman

29. CCG derivation NP/N:a N:spokesman S\NP:lied ------------------------------- (FA) NP: a spokesman ---------------------------------------- (BA)

30. CCG derivation NP/N:a N:spokesmanS\NP:lied ------------------------------- (FA) NP: a spokesman ---------------------------------------- (BA) S: a spokesman lied

31. CCG derivation NP/N:a N:spokesman S\NP:lied ------------------------------- (FA) NP: a spokesman ---------------------------------------- (BA) S: a spokesman lied

32. Coordination in CCG np:Artie(s\np)/np:likes(x\x)/x:and np:Tony(s\np)/np:hates np:beans ---------------- (TR) ---------------- (TR) s/(s\np):Artie s/(s\np):Tony ------------------------------------ (FC)--------------------------------------- (FC) s/np: Artie likes s/np:Tony hates ------------------------------------------------------- (FA) (s/np)\(s/np):and Tony hates --------------------------------------------------------------------------------- (BA) s/np: Artie likes and Tony hates ------------------------------------------------------ (FA) s: Artie likes and Tony hates beans

33. The Glue • Use the Lambda Calculus to combine CCG with DRT • Each lexical entry gets a DRS with lambda-bound variables, representing the “missing” information • Each combinatorial rule in CCG gets a semantic interpretation, again using the tools of the lambda calculus

34. Interpreting Combinatorial Rules • Each combinatorial rule in CCG is expressed in terms of the lambda calculus: • Forward Application:FA(,) = @ • Backward Application:BA(,) = @ • Type Raising:TR() = x.x@ • Function Composition:FC(,) = x.@x@

35. CCG: lexical semantics

36. CCG derivation NP/N:a N:spokesman S\NP:lied p. q. ;p@x;q@x z. x.x@y.

37. CCG derivation NP/N:a N:spokesman S\NP:lied p. q. ;p@x;q@x z. x.x@y. ------------------------------------------------ (FA) NP: a spokesman p. q. ;p@x;q@x@z.

38. CCG derivation NP/N:a N:spokesman S\NP:lied p. q. ;p@x;q@x z. x.x@y. -------------------------------------------------------- (FA) NP: a spokesman q. ; ;q@x

39. CCG derivation NP/N:a N:spokesman S\NP:lied p. q. ;p@x;q@x z. x.x@y. -------------------------------------------------------- (FA) NP: a spokesman q. ;q@x

40. CCG derivation NP/N:a N:spokesman S\NP:lied p. q. ;p@x;q@x z. x.x@y. -------------------------------------------------------- (FA) NP: a spokesman q. ;q@x -------------------------------------------------------------------------------- (BA) S: a spokesman lied x.x@y. @q. ;q@x

41. CCG derivation NP/N:a N:spokesman S\NP:lied p. q. ;p@x;q@x z. x.x@y. -------------------------------------------------------- (FA) NP: a spokesman q. ;q@x -------------------------------------------------------------------------------- (BA) S: a spokesman lied q. ;q@x @ y.

42. CCG derivation NP/N:a N:spokesman S\NP:lied p. q. ;p@x;q@x z. x.x@y. -------------------------------------------------------- (FA) NP: a spokesman q. ;q@x -------------------------------------------------------------------------------- (BA) S: a spokesman lied ;

43. CCG derivation NP/N:a N:spokesman S\NP:lied p. q. ;p@x;q@x z. x.x@y. -------------------------------------------------------- (FA) NP: a spokesman q. ;q@x -------------------------------------------------------------------------------- (BA) S: a spokesman lied

44. The Clark & Curran Parser • Use standard statistical techniques • Robust wide-coverage parser • Clark & Curran (ACL 2004) • Grammar derived from CCGbank • 409 different categories • Hockenmaier & Steedman (ACL 2002) • Results: 96% coverage WSJ • Bos et al. (COLING 2004) • Example output:

45. Applications • Has been used for different kind of applications • Question Answering • Recognising Textual Entailment

46. Recognising Textual Entailment • A task for NLP systems to recognise entailment between two (short) texts • Introduced in 2004/2005 as part of the PASCAL Network of Excellence • Proved to be a difficult, but popular task. • Pascal provided a development and test set of several hundred examples

47. RTE Example (entailment) RTE 1977 (TRUE) His family has steadfastly denied the charges. ----------------------------------------------------- The charges were denied by his family.

48. RTE Example (no entailment) RTE 2030 (FALSE) Lyon is actually the gastronomical capital of France. ----------------------------------------------------- Lyon is the capital of France.

49. Aristotle’s Syllogisms ARISTOTLE 1 (TRUE) All men are mortal. Socrates is a man. ------------------------------- Socrates is mortal.

50. How to deal with RTE • There are several methods • We will look at five of them to see how difficult RTE actually is